5 March 2018 Automatic newly increased built-up area extraction from high-resolution remote sensing images using line-density-based visual saliency and PanTex
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Abstract
Feature-based change detection technologies using multitemporal remote sensing images are widely applied to find newly increased built-up areas (NIBUA) during the period of observation. This paper proposes an automatic object-based NIBUA extraction method using high-resolution remote sensing images, which is based on the integration of spectrum feature, edge-derived line-density-based visual saliency (LDVS) feature, and texture-derived built-up presence index (PanTex) feature. In the proposed method, image segmentation is first employed to obtain objects as basic units of detection. Next, due to the complexity of built-up areas in high-resolution images, LDVS images and PanTex images are produced for each temporal image, respectively. Then, to highlight built-up areas in complex scenes, a comprehensive measure for each object is calculated by integrating the newly increased measures from spectrum, LDVS, and PanTex features via a manner of Dempster–Shafer evidence fusion. Finally, the object-based NIBUA can be extracted by conducting binarization on the newly increased fused measure image. Comparison studies and experimental results demonstrate that our method can achieve a robust extraction of NIBUA from high-resolution remote sensing images with a higher detection accuracy. We conclude that this automatic way can play a positive role in reducing the artificial workload of the interpreters and the cost of monitoring a large-region area. It is encouraged to employ this method in a variety of applications, such as illegal construction land monitoring, land use/cover map update, and city planning.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Tianjun Wu, Tianjun Wu, Jiancheng Luo, Jiancheng Luo, Xiaocheng Zhou, Xiaocheng Zhou, Jianghong Ma, Jianghong Ma, Xueli Song, Xueli Song, } "Automatic newly increased built-up area extraction from high-resolution remote sensing images using line-density-based visual saliency and PanTex," Journal of Applied Remote Sensing 12(1), 015016 (5 March 2018). https://doi.org/10.1117/1.JRS.12.015016 . Submission: Received: 26 October 2017; Accepted: 9 February 2018
Received: 26 October 2017; Accepted: 9 February 2018; Published: 5 March 2018
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